Surgical site complications in kidney transplant recipients: incidence, risk factors and outcomes in the modern era
Bibliographic record
Abstract
BACKGROUND: Surgical site complications (SSCs) are an important source of morbidity after kidney transplantation. We assessed the incidence, risk factors, outcomes and economic impact of SSCs in a large, diverse population of kidney transplant recipients. METHODS: We conducted a single-centre, observational cohort study of adult (age ≥ 18 yr) patients who underwent kidney transplantation between Jan. 1, 2005, and Dec. 31, 2015, with a minimum of 1 year of follow-up. Cases of SSC, including infections and wound dehiscence, were determined from patient records. Inpatient and outpatient hospital costs were determined 6 and 12 months after transplantation. We used the Kaplan-Meier product-limit method to determine the cumulative probability of SSCs and other outcomes. We evaluated risk factors and clinical outcomes using Cox proportional hazard ratios. Linear regression models were used to study the effect of SSCs on graft function. RESULTS: The incidence rate of SSCs within 30 days after transplantation was 4.19 per 100 person-months. The cumulative probability of developing an SSC within 30 days after transplantation was 4.13% (95% confidence interval [CI] 3.23%-5.28%). Increased recipient body mass index (BMI) (hazard ratio [HR] 1.07, 95% CI 1.02-1.11), longer cold ischemic time (HR 1.05, 95% CI 1.01-1.09) and transplantation in 2010-2012 versus 2005-2009 (HR 2.20, 95% CI 1.19-4.04) were risk factors for SSC development. In multivariable stepwise Cox proportional hazard models, SSC was a significant risk factor for death-censored graft failure (HR 3.08, 95% CI 1.60-5.90) and total graft failure (HR 2.09, 95% CI 1.32-3.32). Cumulative median hospital costs were $2238.46 greater for patients with an SSC than for those without. CONCLUSION: Increased BMI, longer cold ischemic time and the 2010-2012 transplantation period predisposed to SSCs. The development of SSCs was associated with a higher risk of graft failure. Strategies to minimize SSCs may improve outcomes after kidney transplantation and reduce costs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".